Robust Text-to-SQL Generation with Execution-Guided Decoding
This improves text-to-SQL systems for database querying, but it is incremental as it builds on existing models.
The paper tackles the problem of neural semantic parsing for text-to-SQL generation by introducing execution-guided decoding to detect and exclude faulty programs during decoding, achieving a new state-of-the-art execution accuracy of 83.8% on WikiSQL.
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to leverage the semantics of SQL. It detects and excludes faulty programs during the decoding procedure by conditioning on the execution of partially generated program. The mechanism can be used with any autoregressive generative model, which we demonstrate on four state-of-the-art recurrent or template-based semantic parsing models. We demonstrate that execution guidance universally improves model performance on various text-to-SQL datasets with different scales and query complexity: WikiSQL, ATIS, and GeoQuery. As a result, we achieve new state-of-the-art execution accuracy of 83.8% on WikiSQL.